Pressure Drop Analysis Using Deep Learning Techniques to Enhance Micro-channel Cooling Performance in Energy Conversion Systems
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Abstract
The high power energy conversion systems are quickly increasing in power, which requires a great deal of efficiency in thermal management, and micro-channel heat sinks have found considerable use in this area. But it is computationally expensive and time consuming to determine the pressure drop in these complicated micro-geometries accurately by using the conventional experimental or computational fluid dynamics methodology. In a bid to overcome this critical issue, this work suggests a new deep learning model, which is referred to as the Channel-Inspired Neural Network (CoINN), which is specially designed to identify pressure drop in the variety of micro-channel arrangements. A large dataset was created and described that was based on physics, and it included different operational fluid dynamics and geometric parameters. CoINN model was trained and strictly benchmarked against known machine learning baseline models, such as Gradient Boosted Regression Trees, shallow Artificial Neural Network, and Support Vector machine. The CoINN framework has been shown by quantitative analysis to be absolutely superior with a Coefficient of Determination of 0.9998 and a Mean Absolute Error of only 0.859 kPa which means that CoINN would compare with the traditional algorithms that would have trouble with the non-linearities inherent with the fluid data. Moreover, systematic sensitivity analysis confirmed the physical interpretability of the deep learning model and accurately found the two key physical mechanisms governing the pressure drop due to frictional inlet velocity and hydraulic diameter. The suggested framework offers a very robust, predictive surrogate model, which is accurate and computationally fast, and it opens the way to faster design and optimization of advanced micro-scale cooling technologies.
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